1. In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in future. 100 years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set - identify the most important problems and focus on them - is still important. It became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction.

2. Wlodzislaw Duch, What Is Computational Intelligence and Where Is It Going? Jurgen Schmidhuber, New Millennium AI and the Convergence of History Ron Sun, The Challenges of Building Computational Cognitive Architectures James A. Anderson et al. Programming a Parallel Computer: The Ersatz Brain Project JG Taylor, The Human Brain as a Hierarchical Intelligent Control System Soo-Young Lee, Artificial Brain and OfficeMateTR based on Brain Information Processing Mechanism Stan Gielen, Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour DeLiang Wang, Computational Scene Analysis

3. Nikola Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities Robert P.W. Duin, Elżbieta Pękalska, The Science of Pattern Recognition. Achievements and Perspectives Wlodzislaw Duch, Towards Comprehensive Foundations of Computational Intelligence Witold Pedrycz, Knowledge-Based Clustering in Computational Intelligence Vera Kurkova, Generalization in Learning from Examples Lei Xu, A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective Jacek Mańdziuk, Computational Intelligence in Mind Games Xindi Cai and Donald C. Wunsch II, Computer Go: A Grand Challenge to AI Lipo Wang and Haixiang Shi, Noisy Chaotic Neural Networks for Combinatorial Optimization

4. Grand challenges Our discipline is broad, and there many grand challenges for the next 20 years. Foundations for CI theory, integrating all methods. Learning from data in difficult cases Complex models, structured data, natural perception Understanding brain/mind relations, neuromorphic models Natural language processing Combining CI (perception) with AI (systematic reasoning) Towards artificial minds Artificial Minds (AMs), or personoids, are software and robotic agents that humans can talk to and relate to in a similar way as they relate to other humans. Neurocognitive informatics!

5. Crises of the richness Hundreds of components... transforming, visualizing... Yale 3.3: type # components Data preprocessing 74 Experiment operations 35 Learning methods 114 Metaoptimization schemes 17 Postprocessing 5 Performance validation 14 Visualization, presentation, plugins... Visual “knowledge flow” to link components, or script languages (XML) to define complex experiments. Are new methods better than what we already have in our treasure box? How can we be sure?

6. Meta-learning as search in model space Search in a well-defined transformation framework, from the simplest kNN to novel combination of procedures & parameterizations. k-NN 67.5/76.6% +d(x,y); Canberra 89.9/90.7 % + s i =(0,0,1,0,1,1); 71.6/64.4 % +selection, 67.5/76.6 % +k opt; 67.5/76.6 % +d(x,y) + s i =(1,0,1,0.6,0.9,1); Canberra 74.6/72.9 % +d(x,y) + sel. or opt k; Canberra 89.9/90.7 % k-NN 67.5/76.6% +d(x,y); Canberra 89.9/90.7 % + s i =(0,0,1,0,1,1); 71.6/64.4 % +selection, 67.5/76.6 % +k opt; 67.5/76.6 % +d(x,y) + s i =(1,0,1,0.6,0.9,1); Canberra 74.6/72.9 % +d(x,y) + selection; Canberra 89.9/90.7 %

7. Difficult cases: complex logic For n bits there are 2 n nodes; in extreme cases such as parity all neighbors are from the wrong class, so localized networks will fail. Achieving linear separability without special architecture may be impossible. Redefining goal of learning and defining complexity classes: the concept of k-separability.

8. DREAM modules Natural input modules Cognitive functions Affective functions Web/text/ databases interface Behavior control Control of devices Talking head Text to speech NLP functions Specialized agents Natural perception requires many specialized transformations, not genera learning techniques; cognitive functions go beyond pattern recognition, to learning from partial observations and systematic reasoning.

9. Humanized interfaces: graphics or android heads Wlodzislaw Duch, Google: Duch NTU Store Applications, eg. 20 questions game & other word games, medical systems Query Semantic memory for artificial minds: integration of perception and cognition Parser POS taggers, phrases, NLP connectionist systems On line dictionaries, encyclopedias, ontologies, free text sources … Manual cleaning, collaborative knowledge acquisition verification

10. HIT related areas HIT projects T-T-S synthesis Speech recognition Talking heads Behavioral models Graphics Cognitive Architectures Cognitive science AI A-Minds A-Minds Lingu-bots Knowledge modeling Info-retrieval VR avatars Robotics Brain models Affective computing Episodic Memory Semantic memory Working Memory Learning

11. Cognitive Systems What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence, as beyond pattern matching, classification and prediction. Low level cognitive functions: perception, sensorimotor actions, are basically active signal analysis (control used to get better signal) + active pattern matching (anticipation, attention, information filtering) to recognize objects and structures. Higher-level cognitive functions: associative and episodic memory for natural perception, representation of complex knowledge structures, sequential logical and intuitive reasoning processes, problems solving, planning and other things symbolic AI works on... In between? Reinforcement learning, emotions? Intuitive computing, solving compositionality problems – search constrained by separable neural networks.

12. Cognitive Systems How can such machine intelligence best be employed? How can such machine intelligence best be employed? There are already numerous educational + industrial applications, more are coming in home and office automation, cars (vision and object recognition, planning routes) etc. Driving in urban environment requires some pre-symbolic reasoning. We need a detailed roadmap with progressively more difficult tasks: what has been already done and may be integrated in other models to avoid duplication of work (although sometimes it is useful), may be used in applications & improved; sound/object localization, orientation mechanisms, control, recognitions of speech, gestures, lip movements, face recognition, person identification, etc; what has been already done and may be integrated in other models to avoid duplication of work (although sometimes it is useful), may be used in applications & improved; sound/object localization, orientation mechanisms, control, recognitions of speech, gestures, lip movements, face recognition, person identification, etc; what is doable in relatively short time – some emotions, object recognition, attention control; what is doable in relatively short time – some emotions, object recognition, attention control; what is difficult – neural approach to higher mental functions? what is difficult – neural approach to higher mental functions?

13. Cognitive Systems How is intelligence actually achieved in the human brain (for example as related to recent researches on the capacity and power of human working memory)? How is intelligence actually achieved in the human brain (for example as related to recent researches on the capacity and power of human working memory)? Depending on the level. Perception, motor control – good models of some functions. Higher cognitive functions - no one really knows? How is reasoning achieved without language? How is reasoning achieved without language? General idea: at the base level, spreading activation networks, particular configuration of activation distributions represents the object at microlevel; different hierarchical levels of search, left/right hemisphere interactions – interesting experimental data from paired word associations and solving problems requiring insight. General principle: learning new by re-using old.

14. Cognitive Systems What are general simple architectures that support reasoning? What are general simple architectures that support reasoning? Classical symbolic: SOAR, ACT-R, have large number of applications, although they are very rough approximations to brain processes. Interesting connectionist architectures: IDA (Franklin), Shruti (Shastri) and many others. Comparison of some architectures in real-time robot control applications would be useful. How can we implement primitive levels of reasoning as are observed in crows and chimpanzees? How can we implement primitive levels of reasoning as are observed in crows and chimpanzees? Animal reasoning is pre-symbolic, so first sensorimotor exploration is needed, involving object and motion recognition + solving simple manipulation problems.

15. Cognitive Systems Does language play an essential role in the reasoning process (sometimes hidden)? Does language play an essential role in the reasoning process (sometimes hidden)? Representation of real objects and sensomotoric sequences in terms of activations has large variability, adding symbolic labels reduces variability in the part of activation space. This must influence the reasoning process. How can we build a truly creative architecture to solve difficult tasks? How can we build a truly creative architecture to solve difficult tasks? I’ve proposed (WCCI’06) to focus first on creation of new words, starting from description of products, organizations etc, simulating the process, as our simulations find some interesting words and about 2/3 words that have already been invented. This can be extended to higher-level mechanisms, as in Mazursky, Goldberg and Solomon work on ideas for advertisement.

16. Cognitive Systems How would a reasoning system at non-linguistic level help in any branch of industry? How would a reasoning system at non-linguistic level help in any branch of industry? One example is in understanding complex machinery reactions, as in the refineries or other plants; this is relatively simple and may be achieved using correlation machines. Car driving in urban environments will need some reasoning. What are the ethical problems thrown up by future advances in this area, advancing as it does towards the 'soul' of humanity? What are the ethical problems thrown up by future advances in this area, advancing as it does towards the 'soul' of humanity? People are very resistant to science and will harbor their ideas about souls and spirits independent of the development... Problems may arise in distant future when more and more jobs will be automated. Conscious machines will open a Pandora’s box...